How To Use Big Data To Develop An Effective Workplace Strategy

Written by Sandra Panara, Director of Workspace Insights

When it comes to assessing workspace utilization, there are many approaches that could be taken to generate data for analysis. Whether the data is generated using employee surveys, observation studies, security badge data or even sensor data, each source of data provides the initial validation necessary to ensure that your workplace strategy is directionally correct. However, the use of utilization data, although foundational, is only one piece of a very complex puzzle.

The best workspace related insights come from data observed at the lowest level of detail. Where many equate this to the identity level of the person using space, the reality is the actual identity of the person is irrelevant. What does matter though is the attributes associated with the person, which is then analyzed in aggregate, to identify unique patterns and trends.

A critical success factor in working with data sources is data sharing between Corporate Real Estate (CRE), HR and IT. In fact, a successful Workplace Strategy cannot be implemented without an in-depth understanding of key data that is owned and/or managed by these three teams in an organization.

This blog will help you understand the different kinds of data sources available, the various challenges that often arise when working with multiple sources, and how to collaborate with other departments to successfully blend that data together.

5 Common Types Of Data Sources

First, let’s review the five main buckets of data often used to develop a proper Workplace Strategy:

Such initiatives may include people counters or collecting data from varying sensor types, to provide insights about specified locations in your building like a meeting room, workstation, private office, lunchroom or lounge.

This type of data is acquired through a sensor-as-a-service provider such as Relogix. When installed, sensors capture data in real-time and record occupancy, utilization and dwell time. The end result is a clear understanding of the amount of time that various workspaces and rooms in the building are occupied and in use.

This kind of data is publicly accessible and can help support current and future location decisions. It may include collaboration patterns (facilitated by IT) to illustrate adjacency requirements more accurately. Another option may be census (facilitated by HR) or market-related data (facilitated by CRE), which can provide indicators related to viability of growth in existing markets; or present opportunities by illustrating trends related to the labor market, market rent and other data.

Purpose Of Individual Data Sets

Each data set on its own serves a very specific purpose in your organization. For example:

Your leasing team might use leasing data to manage rent payments or lease renewals, or to provide the required notice of intent to vacate for leases they don’t plan to renew.

Your security team might use security badge swipe data to ensure authorized employees access authorized buildings and spaces within those buildings for security audit purposes. They may also be looking for odd, potentially fraudulent behaviors, like duplicate badge ID numbers assigned to different users who might be accessing separate locations in different parts of the country on the same day.

Your HR team might use data about its employees to perform workforce planning strategies. They may be performing people analytics; observing trends and patterns related to the make-up of the organization such as gender, age, and tenure, education level, skills or compensation; or might be using the data to identify gaps so they can adjust their hiring strategies to better plan for the future.

Your CRE team may already be accessing some of this data, like the security badge swipe data, to illustrate building and/or floor occupancy, as well as information from HR like business area for example, which enables grouping of the data to to provide a bit of context about who those users might be.

Blending Data Sources To Reveal Amazing Insights

Being able to blend multiple data sources effectively provides instant value by providing the necessary level of detail which significantly improves the context to your analysis.

Rather than just reporting the percentage of time employees in a particular business unit are in a building or on a floor, or even at their desk, with blended data you could then answer questions like:

Does employee age or tenure impact space utilization?

Does utilization vary by job function/title or even job level?

Is there an existing or emerging preference related to spaces used by varying demographics?

You might also be able to associate other pieces of information about your employees that enable you to perform commuting patterns, which would further allow you to answer questions like:

Do employees who live closer to the office utilize space more or less than those who live further away?

Do employees access office locations closer to their home rather than the location to which they are assigned and if they do, how often?

Observing data at the appropriate level of detail allows you to validate the many theories you may have read about in published research papers or studies.

That said, it is always more effective to plan space based on the reality of your organization because your planning efforts are tailored to be culturally relevant to your business.

Building ABC located in New York City has an assigned population of 5,000 employees. It’s occupied on average 65% of the time (~ 3,250 employees).

When augmented with complementary information the statement above will start to look something like the following:

15% of those with presence detected at Building X are Boomers, 40% are Gen X, 35% are Millennials, and 10% are Gen Z. On average, Boomers and Gen X combined come to the office just as frequently as Millennials and Gen Z. Gen X and Gen Z live within 20 Km of the office. However, Gen Z comes to the office 90% of the time. 100% of Gen Z have been with the company less than 1 year. In all instances, employees who have been with the company for less than two years come to the office 80% of the time vs. other groups who come to the office significantly less frequently.

As you can see, when a few, many or all data sources about both your workspace and your people are successfully combined, amazing insights begin to emerge. This enables you to:

Qualify and quantify Real Estate, HR, IT and Finance-related opportunities

Develop informed strategies

Demonstrate the potential impact a transformation can have in your organization

However, data blending is not an easy feat. Despite all the amazing insights you can garner from using multiple data sources, bringing them all together will elevate several issues.

Challenge #1: Working With Different Formats

Not only are you trying to blend disparate data points: you also must consider the formats in which they are available (e.g., Excel, text, PDF, PowerPoint, CSV, JSON, and many more).

In most organizations, the first attempt at data blending is usually tackled using Excel. Key data is lifted from non-tabular data sources and put into a tabular format so that the data becomes usable.

Challenge #2: Dealing With Different Levels Of Detail

One of the first challenges you will likely encounter as the data begins to come together, is the inconsistent levels of detail that each data set contains—creating gaps in data.

For example, consider trying to assess workspace utilization with the following three data sets:

Your security badge data may be provided at the individual level, or it may be provided at the building level.

Your leasing data may be at the building level and the floor level (and there may be different lease expiry dates for different floors in one building).

Your employee data may be shared at the building level only, so you may not be able to know precisely the makeup of each floor.

While challenging, blending data with different levels of detail can be achieved and meaningful insights can still be extracted to yield the best possible outcomes. However, this requires a vision and careful planning once you know what data you can use. Understanding this is a critical first step, since it identifies how deep your analysis can go.

Challenge #3: Overcoming Data Privacy And Security Concerns

In order to work with all the data you intend to bring together, you will need to speak with your IT and/or security team.

If you are storing or sharing the data electronically or on the cloud, IT will most definitely need to be involved to assess any risks to the business related to the data. This will likely involve intense scrutiny by IT, not to mention HR, who will have concerns related to the potential risks associated with data security and privacy. While necessary, any data security concerns raised may stall your project before it even begins.

Challenge #4: Respecting Employees’ Rights To Privacy

Data collection in the workplace is not new, but it is evolving. Depending on your corporate culture, employees may react negatively to knowing that they are being tracked or monitored—and may even express feeling like “Big Brother is watching.”

Proactively communicating the following details to employees is a vital requirement that often gets overlooked. However, employees should absolutely know:

What data is/will be collected

Why it’s being collected

Who will have access to the data

How it will be used

It is also important to emphasize that the significance of the insights extracted from this data is not at the individual level but rather at the organizational level, to enable you to provide the tools and technologies that best support the needs of employees. Certainly, the analysis happens at the most granular level; however, the reporting should not, unless there is a specifically authorized business reason to do so.

Taking Your Data Sources To The Next Level

Now that you understand the basics of the data sources that may be available to you, you may be wondering what to do with this newfound knowledge.

In future blog posts, we will look at the “how-to” of blending data and overcoming all the technological and organizational challenges that often come with it—including how to extract the information you need to build a sound business case for your Workplace Strategy initiative, and which your senior leadership team won’t be able to ignore.

About the Author

Sandra Panara, Director of Workspace Insights

Sandra is known for her deep understanding of Corporate Real Estate and Technology. With over 25 years hands-on experience in North America and the UK with RBC, Purolator, The Coca Cola Company and more. Sandra applies non-traditional approaches to extract deep learning from the most unsuspecting places in order to drive strategy. She has developed an appreciation for always challenging the status quo to provoke and encourage new ways of thinking that drive continuous improvement and innovation. Sandra believes square pegs can fit into round holes and that the real ‘misfits’ are environments that fail to adapt. Her expertise ranges broadly from CRE Portfolio analysis & Insights, Workforce Planning, Strategy Development to Space & Occupancy Planning.

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Cookie settingsACCEPT

Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.

Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.

Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.